Application of metaheuristic and deterministic algorithms for aircraft reference trajectory optimization

Aircraft reference trajectory is an alternative method to reduce fuel consumption, thus the pollution released to the atmosphere. Fuel consumption reduction is of special importance for two reasons: first, because the aeronautical industry is responsible of 2% of the CO2 released to the atmosphere, and second, because it will reduce the flight cost. The aircraft fuel model was obtained from a numerical performance database which was created and validated by our industrial partner from flight experimental test data. A new methodology using the numerical database was proposed in this thesis to compute the fuel burn for a given trajectory. Weather parameters such as wind and temperature were taken into account as they have an important effect in fuel burn. The open source model used to obtain the weather forecast was provided by Weather Canada. A combination of linear and bi-linear interpolations allowed finding the required weather data. The search space was modelled using different graphs: one graph was used for mapping the different flight phases such as climb, cruise and descent, and another graph was used for mapping the physical space in which the aircraft would perform its flight. The trajectory was optimized in its vertical reference trajectory using the Beam Search algorithm, and a combination of the Beam Search algorithm with a search space reduction technique. The trajectory was optimized simultaneously for the vertical and lateral reference navigation plans while fulfilling a Required Time of Arrival constraint using three different metaheuristic algorithms: the artificial bee’s colony, and the ant colony optimization. Results were validated using the software FlightSIM®, a commercial Flight Management System, an exhaustive search algorithm, and as flown flights obtained from flightaware®. All algorithms were able to reduce the fuel burn, and the flight costs

Horizontal flight trajectory optimization considering RTA constraints

The increasing of flights around the world has led to various problems for the aeronautical industry such as saturated air space and higher levels of fossil fuel consumption. The way in which en-route flights are handled should be improved in order to increase airways’ capacity. A solution is to make aircraft to arrive at specific waypoints at a time constraint called Required Time of Arrival (RTA). Fossil fuel brings as a consequence the release of polluting particles to the atmosphere such as carbon dioxide and nitrogen oxides. It is thus desirable to compute the most economical trajectory in terms of fuel burn while fulfilling the RTA constraint. This article proposes a horizontal reference trajectory optimization algorithm based on the Particle Swarm Optimization technique in order to reduce fuel burn while fulfilling the RTA constraint. Results showed that for a flight without RTA constraint, up to 4% of fuel can be saved comparing against the trajectory of reference. The algorithm was normally able to meet the RTA constrain. However, aggressive RTA constraints might reduce the optimization levels of fuel compared with flights without RTA constraint

New Search Space Reduction Algorithm for Vertical Reference Trajectory Optimization

Burning the fuel required to sustain a given flight releases pollution such as carbon dioxide and nitrogen oxides, and the amount of fuel consumed is also a significant expense for airlines. It is desirable to reduce fuel consumption to reduce both pollution and flight costs. To increase fuel savings in a given flight, one option is to compute the most economical vertical reference trajectory (or flight plan). A deterministic algorithm was developed using a numerical aircraft performance model to determine the most economical vertical flight profile considering take-off weight, flight distance, step climb and weather conditions. This algorithm is based on linear interpolations of the performance model using the Lagrange interpolation method. The algorithm downloads the latest available forecast from Environment Canada according to the departure date and flight coordinates, and calculates the optimal trajectory taking into account the effects of wind and temperature. Techniques to avoid unnecessary calculations are implemented to reduce the computation time. The costs of the reference trajectories proposed by the algorithm are compared with the costs of the reference trajectories proposed by a commercial flight management system using the fuel consumption estimated by the FlightSim® simulator made by Presagis®

Horizontal flight trajectory optimization considering RTA constraints

The increasing of flights around the world has led to various problems for the aeronautical industry such as saturated air space and higher levels of fossil fuel consumption. The way in which en-route flights are handled should be improved in order to increase airways’ capacity. A solution is to make aircraft to arrive at specific waypoints at a time constraint called Required Time of Arrival (RTA). Fossil fuel brings as a consequence the release of polluting particles to the atmosphere such as carbon dioxide and nitrogen oxides. It is thus desirable to compute the most economical trajectory in terms of fuel burn while fulfilling the RTA constraint. This article proposes a horizontal reference trajectory optimization algorithm based on the Particle Swarm Optimization technique in order to reduce fuel burn while fulfilling the RTA constraint. Results showed that for a flight without RTA constraint, up to 4% of fuel can be saved comparing against the trajectory of reference. The algorithm was normally able to meet the RTA constrain. However, aggressive RTA constraints might reduce the optimization levels of fuel compared with flights without RTA constraint

3D Cruise Trajectory Optimization Inspired by a Shortest Path Algorithm

Aircrafts require a large amount of fuel in order to generate enough power to perform a flight. That consumption causes the emission of polluting particles such as carbon dioxide, which is implicated in global warming. This paper proposes an algorithm which can provide the 3D reference trajectory that minimizes the flight costs and the fuel consumption. The proposed algorithm was conceived using the Floyd–Warshall methodology as a reference. Weather was taken into account by using forecasts provided by Weather Canada. The search space was modeled as a directional weighted graph. Fuel burn was computed using the Base of Aircraft DAta (BADA) model developed by Eurocontrol. The trajectories delivered by the developed algorithm were compared to long-haul flight plans computed by a European airliner and to as-flown trajectories obtained from Flightradar24®. The results reveal that up to 2000 kg of fuel can be reduced per flight, and flight time can be also reduced by up to 11 min

Enhancing a simulation-based Allocator Software with machine learning and genetic algorithms for improving the gate assignment problem

Assigning gates to flights considering physical, operational, and temporal constraints is known as the Gate Assignment Problem. This article proposes the novelty of coupling a commercial stand and gate allocation software with an off-the-grid optimization algorithm. The software provides the assignment costs, verifies constraints and restrictions of an airport, and provides an initial allocation solution. The gate assignment problem was solved using a genetic algorithm. To improve the robustness of the allocation results, delays and early arrivals are predicted using a random forest regressor, a machine learning technique and in turn they are considered by the optimization algorithm. Weather data and schedules were obtained from Zurich International Airport. Results showed that the combination of the techniques result in more efficient and robust solutions with higher degree of applicability than the one possible with the sole use of them independently

Analysis of security lines policies for improving capacity in airports: Mexico City case

With the increase of needs for controlling the passengers that use different modes of transport such as airports, ports, trains, or future ones as hyper loops, security facilities are a key element to be optimized. In the current study, we present an analysis of a security area within an airport with particular restrictions. To improve the capacity, different categories and policies were devised for processing passengers and we propose to adapt the system to these categories and policies. The results indicated that, by designing a proper category in combination with novel technology, it is possible to increase the capacity to values of 2 digits (in terms of passengers/day). As a proof-of-concept, we use a case study of an area within an airport in Mexico based on data and layout of early 2019

Airport passenger flow prediction using LTSM Recurrent Neural Networks

Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators

Commercial aircraft trajectory optimization to reduce flight costs and pollution: metaheuristic algorithms

Aircraft require significant quantities of fuel in order to generate the power required to sustain a flight. Burning this fuel causes the release of polluting particles to the atmosphere and constitutes a direct cost attributed to fuel consumption. The optimization of various aircraft operations in different flight phases such as cruise and descent, as well as terminal area movements, have been identified as a way to reduce fuel requirements, thus reducing pollution. The goal of this chapter is to briefly explain and apply different metaheuristic optimization algorithms to improve the cruise flight phase cost in terms of fuel burn. Another goal is to present an overview of the most popular commercial aircraft models. The algorithms implemented for different optimization strategies are genetic algorithms, the artificial bee colony, and the ant colony algorithm. The fuel burn aircraft model used here is in the form of a Performance Database. A methodology to create this model using a Level D aircraft research flight simulator is briefly explained. Weather plays an important role in flight optimization, and so this work explains a method for incorporating open source weather. The results obtained for the optimization algorithms show that every optimization algorithm was able to reduce the flight consumption, thereby reducing the pollution emissions and contributing to airlines’ profit margins

The attack and defense on aircraft trajectory prediction algorithms

The aviation industry needs led to an increase in the number of aircraft in the sky. When the number of flights within an airspace increases, the chance of a mid-air collision increases. Systems such as the Traffic Alert and Collision Avoidance System (TCAS) and Airborne Collision Avoidance System (ACAS) are currently used to alert pilots for potential mid-air collisions. The TCAS and the ACAS use algorithms to perform Aircraft Trajectory Predictions (ATPs) to detect potential conflicts between aircrafts. In this paper, three different aircraft trajectory prediction algorithms named Deep Neural Network (DNN), Random Forest (RF) and Extreme Gradient Boosting were implemented and evaluated in terms of their accuracy and robustness to predict the future aircraft heading. These algorithms were as well evaluated in the case of adversarial samples. Adversarial training is applied as defense method in order to increase the robustness of ATPs algorithms against the adversarial samples. Results showed that, comparing the three algorithm’s performance, the extreme gradient boosting algorithm was the most robust against adversarial samples and adversarial training may benefit the robustness of the algorithms against lower intense adversarial samples. The contributions of this paper concern the evaluation of different aircraft trajectory prediction algorithms, the exploration of the effects of adversarial attacks, and the effect of the defense against adversarial samples with low perturbation compared to no defense mechanism